用扩散模型构建树状草稿，提升自回归语言模型的推理速度。
Accelerating Speculative Decoding with Block Diffusion Draft Trees
- 基于扩散模型生成草稿块，并构建多路径草稿树。
- 在固定节点预算下，通过优先级算法选择最可能匹配目标模型的路径。
- 单次前向传播完成树结构验证，适合高吞吐场景使用。
推测解码通过轻量级草稿模型预测多个未来词元，由目标模型并行验证以加速自回归语言模型。DFlash表明，基于块扩散的草稿模型可在一次前向传播中生成整个草稿块，性能优于EAGLE-3等强自回归草稿模型。然而，原始DFlash每轮仅验证单一草稿轨迹，可能限制接受长度。本文提出DDTree（扩散草稿树），直接利用块扩散草稿模型各位置的概率分布构建草稿树。在固定节点预算下，采用简单优先队列算法选择最可能与目标模型匹配的延续路径（基于草稿模型输出定义的代理指标）。最终树结构可通过仅包含祖先的注意力掩码，在一次目标模型前向传播中高效验证。由于依托于领先草稿模型DFlash，DDTree在推测解码中达到顶尖水平。
原文摘要 · Abstract (English)
Speculative decoding accelerates autoregressive language models by using a lightweight drafter to propose multiple future tokens, which the target model then verifies in parallel. DFlash shows that a block diffusion drafter can generate an entire draft block in a single forward pass and achieve state-of-the-art speculative decoding performance, outperforming strong autoregressive drafters such as EAGLE-3. Vanilla DFlash, however, still verifies only a single drafted trajectory per round, potentially limiting its acceptance length. We introduce DDTree (Diffusion Draft Tree), a method that constructs a draft tree directly from the per-position distributions of a block diffusion drafter. Under a fixed node budget, DDTree uses a simple best-first heap algorithm to select the continuations that are most likely to match the target model according to a surrogate defined by the draft model's output. The resulting tree is verified efficiently in a single target model forward pass using an ancestor-only attention mask. Because DDTree builds on DFlash, a leading draft model for speculative decoding, these gains place DDTree among the leading approaches to speculative decoding.
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